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Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database

BACKGROUND: The prognostic factors for survival in patients with ependymoma (EPN) remain controversial. The aim of this study was to establish a prognostic model for 5‐ and 10‐year survival probability nomograms for patients with EPN. METHODS: Clinical data from the Surveillance, Epidemiology, and E...

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Autores principales: Jia, Zetian, Yan, Yaqi, Wang, Jiuxin, Yang, He, Zhan, Haihua, Chen, Qian, He, Yawei, Huang, Changyu, Hu, Yuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419756/
https://www.ncbi.nlm.nih.gov/pubmed/34342153
http://dx.doi.org/10.1002/cam4.4151
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author Jia, Zetian
Yan, Yaqi
Wang, Jiuxin
Yang, He
Zhan, Haihua
Chen, Qian
He, Yawei
Huang, Changyu
Hu, Yuhua
author_facet Jia, Zetian
Yan, Yaqi
Wang, Jiuxin
Yang, He
Zhan, Haihua
Chen, Qian
He, Yawei
Huang, Changyu
Hu, Yuhua
author_sort Jia, Zetian
collection PubMed
description BACKGROUND: The prognostic factors for survival in patients with ependymoma (EPN) remain controversial. The aim of this study was to establish a prognostic model for 5‐ and 10‐year survival probability nomograms for patients with EPN. METHODS: Clinical data from the Surveillance, Epidemiology, and End Results (SEER) database were used for patients diagnosed with ependymoma between 2000 and 2018 and were randomized 7:3 into a development set and a validation set. Factors significantly associated with prognosis were screened out using the least absolute shrinkage and selection operator (LASSO) regression. The calibration chart and consistency index (C‐index) are used to evaluate the discrimination and consistency of the prediction model. Decision curve analysis (DCA) was used to further evaluate the established model. Finally, prognostic factors selected by LASSO regression were evaluated using Kaplan–Meier (KM) survival curves. RESULTS: A total of 3820 patients were included in the prognostic model. Seven survival predictors were obtained by LASSO regression screening, including age, gender, morphology, location, size, laterality, and resection. The prognostic model of the nomogram showed moderate discriminative ability in the development group and the validation group, with a C‐index of 0.642 and 0.615, respectively. In the development set and validation set survival curves, the prognosis index of high risk was less effective than low risk (p < 0.001). CONCLUSIONS: Our nomograms may play an important role in predicting 5 and 10‐year outcomes for patients with ependymoma. This will help assist clinicians in personalized medicine.
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spelling pubmed-84197562021-09-08 Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database Jia, Zetian Yan, Yaqi Wang, Jiuxin Yang, He Zhan, Haihua Chen, Qian He, Yawei Huang, Changyu Hu, Yuhua Cancer Med Bioinformatics BACKGROUND: The prognostic factors for survival in patients with ependymoma (EPN) remain controversial. The aim of this study was to establish a prognostic model for 5‐ and 10‐year survival probability nomograms for patients with EPN. METHODS: Clinical data from the Surveillance, Epidemiology, and End Results (SEER) database were used for patients diagnosed with ependymoma between 2000 and 2018 and were randomized 7:3 into a development set and a validation set. Factors significantly associated with prognosis were screened out using the least absolute shrinkage and selection operator (LASSO) regression. The calibration chart and consistency index (C‐index) are used to evaluate the discrimination and consistency of the prediction model. Decision curve analysis (DCA) was used to further evaluate the established model. Finally, prognostic factors selected by LASSO regression were evaluated using Kaplan–Meier (KM) survival curves. RESULTS: A total of 3820 patients were included in the prognostic model. Seven survival predictors were obtained by LASSO regression screening, including age, gender, morphology, location, size, laterality, and resection. The prognostic model of the nomogram showed moderate discriminative ability in the development group and the validation group, with a C‐index of 0.642 and 0.615, respectively. In the development set and validation set survival curves, the prognosis index of high risk was less effective than low risk (p < 0.001). CONCLUSIONS: Our nomograms may play an important role in predicting 5 and 10‐year outcomes for patients with ependymoma. This will help assist clinicians in personalized medicine. John Wiley and Sons Inc. 2021-08-03 /pmc/articles/PMC8419756/ /pubmed/34342153 http://dx.doi.org/10.1002/cam4.4151 Text en © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Bioinformatics
Jia, Zetian
Yan, Yaqi
Wang, Jiuxin
Yang, He
Zhan, Haihua
Chen, Qian
He, Yawei
Huang, Changyu
Hu, Yuhua
Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database
title Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database
title_full Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database
title_fullStr Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database
title_full_unstemmed Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database
title_short Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database
title_sort development and validation of prognostic nomogram in ependymoma: a retrospective analysis of the seer database
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419756/
https://www.ncbi.nlm.nih.gov/pubmed/34342153
http://dx.doi.org/10.1002/cam4.4151
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